Clinical evidence has demonstrated that the anxiolytic effects produced by the selective 5-hydroxytryptamine1A (5-HT1A) receptor agonist, gepirone, increase progressively over one to three weeks of treatment.

Serotonin has been widely explored in the brain. Recently, there have been new findings on how serotonin works in the periphery. Serotonin is introduced to the periphery by the enterochromaffin cells and metabolized by the liver and lung. Studies have shown that serotonin plays a role in controlling lung cancer. However, the mechanism by which it initiates tumor formation has not been fully explored. Cell viability was measured in several lung adenocarcinoma cell lines treated with serotonin to study this effect. In GFP-labelled cells, fluorescence intensity was measured for quantification of cell viability. Our data showed an overall increase in viability when serotonin concentration was increased, which is significant because it shows that serotonin affects lung cancer progression. We will look at how serotonin works on tumor cells compared to endothelial cells and its effect on immune system activation. This study hopes to inspire future anti-angiogenesis and immunotherapy studies of lung cancer by understanding this interaction.

Serotonin is a normal constituent of the brain and has been claimed to act as a neurohormonal or regulatory agent in nerve transmission. Previous workers have only studied its whole brain or subcellular particulate distribution. Because the brain itself is divided into many distinct ganglia and nuclei, the distribution of serotonin in these sites is considered to be of greater importance than either the whole brain or subcellular distribution. This paper is a study of the distribution of serotonin in particular brain sites in rats with normal and elevated levels of the amine, with and without electroshock treatment. The results indicate: (1) a heterogenous distribution of serotonin and 5-hydroxytryptophan in the rat brain; (2) serotonin and 5-hydroxytryptophan are transported down the neuron axon; (3) serotonin and 5-hydroxytryptophan are not utilized by all neurons; (4) iproniazid and/or electroshock can alter the uptake, distribution and transportation of serotonin and 5-hydroxytryptophan in the different areas and fibers of the central nervous system.

What does serotonin do? Just as the diffuse axonal arbours of midbrain serotonin neurons touch nearly every corner of the forebrain, so too is this ancient neuromodulator involved in nearly every aspect of learning and behaviour. The role of serotonin in reward processing has received increasing attention in recent years, but there is little agreement about how the perplexing responses of serotonin neurons to emotionally salient stimuli should be interpreted, and essentially nothing is known about how they arise. Here I approach these two aspects of serotonergic function in reverse order. In the first part of this thesis, I construct an experimentally-constrained spiking neural network model of the dorsal raphe nucleus (DRN), the main source of forebrain serotonergic input, and characterize its signal processing features. I show that potent spike-frequency adaptation deeply shapes DRN output while other aspects of its physiology are relatively less important. Overall, this part of my work suggests that in vivo serotonergic activity patterns arise from a temporal-derivative-like computation. But the temporal derivative of what? In the second part, I consider the possibility that the DRN is driven by an input that represents cumulative future reward, a quantity called state value in reinforcement learning theory. The resulting model reproduces established tuning features of serotonin neurons, including phasic activation by reward predicting cues and punishments, reward-specific surprise tuning, and tonic modulation by reward and punishment context. Because these features are the basis of many and varied existing serotonergic theories, these results show that my theory, which I call value prediction, provides a unifying perspective on serotonergic function. Finally, in an empirical test of the theory, I re-analyze data from an in vivo trace conditioning experiment and find that value prediction accounts for the firing rates of serotonin neurons to a precision ≪0.1 Hz, outperforming previous models by a large margin. Here I establish serotonin as a new neural substrate of prediction and reward, a significant step towards understanding the role of serotonin signalling in the brain.

This document only includes an excerpt of the corresponding thesis or dissertation. To request a digital scan of the full text, please contact the Ruth Lilly Medical Library's Interlibrary Loan Department (rlmlill@iu.edu).

This thesis pertains to the uses of functional data analysis and nonlinear mixed-effects model with applications to PET data. In the first part of this dissertation, we consider a permutation-based inference for function-on-scalar regression. While PET imaging data analysis is most commonly performed on data that are aggregated into several discrete a priori regions of interest, our primary interest is on measures of 5-HTT binding potential that are made at many locations along a continuous anatomically defined tract, one that was chosen to follow serotonergic axons. Our goal is to characterize the binding patterns along this tract, determine how such patterns differ between diagnostic groups, and also to investigate the question of homogeneity. We utilize function-on-scalar regression modeling to make optimal use of our data and inference is made using permutation testing strategies that do not require distributional assumptions. Simulations are conducted to examine the validity of our methods and compare the performance of competing methods. We illustrate this approach by applying it to PET data. In the second part of this dissertation, we introduce shape-based distance metrics for comparison of IRFs. The common practice involves summarizing the estimated IRF using a single scalar measure, such as VT, and comparing it across subjects/groups using standard univariate analyses. However, this approach neglects the nature and structure of the IRFs and overlooks their shapes. We propose a k-nearest-neighbor ensemble approach that optimally combines distance metrics based on principles of functional data analysis and shape data analysis. Simulations are conducted to compare the predictive performance of our approach to the traditional approach of using VT. We illustrate this approach by applying it to PET data. In the third part of this dissertation, we discuss the a nonlinear mixed-effects modeling approach for PET data analysis under the assumption of a simplified reference tissue model. The conventional two-stage approach uses NLS estimates of the population parameters, although statistically valid, it is possible to allow for more complex models that consider all subjects simultaneously. We propose a nonlinear mixed-effects (NLME) model that can estimate not only the individual-level parameters, but also the effects of covariates on the parameters. In this way, estimation of kinetic parameters and statistical inference can be performed simultaneously. Simulations are conducted to compare the power for detection of group differences and population- and individual-level parameter estimation for both NLS and NLME models. We apply our NLME approach to PET data to illustrate the modeling procedure.

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